EleGNN: Electrical-Model-Guided Graph Neural Networks for Power Distribution System State Estimation
Document Type
Conference Proceeding
Date of Original Version
1-1-2022
Abstract
State estimation plays a critical role in power dis-tribution systems. However, the conventional state estimation commonly used in transmission systems cannot be applied to the distribution systems because of insufficient measurements. Even though machine learning methods have begun to demonstrate their capabilities to overcome this challenge, they are not capa-ble of explicitly incorporating distribution systems' topological information. This paper proposes EleGNN, an electrical-model-guided graph neural network (GNN), to perform power distri-bution system state estimation (DSSE). By explicitly considering physical topology, EleGNN enhances GNN with original node and edge feature propagation methods, allowing us to obtain accurate estimation results despite insufficient measurements and various topologies. Evaluations of six different power systems demonstrate significant improvement in state estimation accuracy than the method relying on general neural networks. Specifically, node-level mean square errors introduced by EleGNN are at least one order of magnitude smaller, even with up to 50% of missing measurements.
Publication Title, e.g., Journal
Proceedings IEEE Global Communications Conference Globecom
Citation/Publisher Attribution
Lin, Hui, and Yan Sun. "EleGNN: Electrical-Model-Guided Graph Neural Networks for Power Distribution System State Estimation." Proceedings IEEE Global Communications Conference Globecom (2022). doi: 10.1109/GLOBECOM48099.2022.10000808.